Development and application of a Monte Carlo method of statistical mechanics is proposed for predicting the secondary structures of single stranded nucleic acid sequences by using thermodynamic "energy rules". In contrast to the traditional minimization routines that calculate only one globally minimum- energy structure, which may or may not have biological significance, Monte Carlo method additionally provides information about other suboptimal structures, and calculates statistically averaged properties. Other advantages of the method include simplicity of the algorithm, requirement of only linear memory storage, and ease of its generalization to include such effects as tertiary interactions. In Phase I, it was demonstrated that the method is capable of predicting biologically significant folding patterns for a large class of RNA molecules, while requiring only modest computational resources. Generalization of the algorithm and development of rapid and reliable strategies for analysis of very large RNA systems is proposed for this Phase II application. Both the method and the results of the proposed studies will be of interest to the scientific and business communities in the health related areas of pharmacology, genetic engineering and molecular biology.